Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Explores the quasi-stationary distribution approach in molecular dynamics modeling, covering Langevin dynamics, metastability, and kinetic Monte Carlo models.
Explores the convergence of Langevin Monte Carlo algorithms under different growth rates and smoothness conditions, emphasizing fast convergence for a wide class of potentials.